An Evolutionary Mutation Testing System for Java Programs: eMuJava

Bashir, Muhammad Bilal; Nadeem, Aamer

doi:10.1007/978-3-030-22868-2_58

Advances in Intelligent Systems and Computing

2019

DOI: 10.1007/978-3-030-22868-2_58

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An Evolutionary Mutation Testing System for Java Programs: eMuJava

Muhammad Bilal Bashir

Aamer Nadeem

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Cited by 2 publications

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“…The performance of this tool is further demonstrated and compared in other prominent studies [51][52][53][54][55].Considering the object's state, Bashir and Nadeem [56] proposed a novel fitness function, which restricts the search process by reviewing the tests that have either obtained the desired state or requires more method calls. The authors in [57][58][59] further extended the work that resulted in the development of a tool named eMuJava. They also compared the relative performances of their proposed variation of GA with traditional GA using ten Java programs of total 1028 LOC.…”

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On the Effectiveness of Using Elitist Genetic Algorithm in Mutation Testing

2019

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Manual test case generation is an exhaustive and time-consuming process. However, automated test data generation may reduce the efforts and assist in creating an adequate test suite embracing predefined goals. The quality of a test suite depends on its fault-finding behavior. Mutants have been widely accepted for simulating the artificial faults that behave similarly to realistic ones for test data generation. In prior studies, the use of search-based techniques has been extensively reported to enhance the quality of test suites. Symmetry, however, can have a detrimental impact on the dynamics of a search-based algorithm, whose performance strongly depends on breaking the “symmetry” of search space by the evolving population. This study implements an elitist Genetic Algorithm (GA) with an improved fitness function to expose maximum faults while also minimizing the cost of testing by generating less complex and asymmetric test cases. It uses the selective mutation strategy to create low-cost artificial faults that result in a lesser number of redundant and equivalent mutants. For evolution, reproduction operator selection is repeatedly guided by the traces of test execution and mutant detection that decides whether to diversify or intensify the previous population of test cases. An iterative elimination of redundant test cases further minimizes the size of the test suite. This study uses 14 Java programs of significant sizes to validate the efficacy of the proposed approach in comparison to Initial Random tests and a widely used evolutionary framework in academia, namely Evosuite. Empirically, our approach is found to be more stable with significant improvement in the test case efficiency of the optimized test suite.

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On the Effectiveness of Using Elitist Genetic Algorithm in Mutation Testing

2019

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Mutation testing in the wild: findings from GitHub

et al. 2022

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Mutation testing exploits artificial faults to measure the adequacy of test suites and guide their improvement. It has become an extremely popular testing technique as evidenced by the vast literature, numerous tools, and research events on the topic. Previous survey papers have successfully compiled the state of research, its evolution, problems, and challenges. However, the use of mutation testing in practice is still largely unexplored. In this paper, we report the results of a thorough study on the use of mutation testing in GitHub projects. Specifically, we first performed a search for mutation testing tools, 127 in total, and we automatically searched the GitHub repositories including evidence of their use. Then, we focused on the top ten most widely used tools, based on the previous results, and manually revised and classified over 3.5K GitHub active repositories importing them. Among other findings, we observed a recent upturn in interest and activity, with Infection (PHP), PIT (Java) and Humbug (PHP) being the most widely used mutation tools in recent years. The predominant use of mutation testing is development, followed by teaching and learning, and research projects, although with significant differences among mutation tools found in the literature—less adopted and largely used in teaching and research—and those found in GitHub only—more popular and more widely used in development. Our work provides a new and encouraging perspective on the state of practice of mutation testing.

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An Evolutionary Mutation Testing System for Java Programs: eMuJava

Cited by 2 publications

References 29 publications

On the Effectiveness of Using Elitist Genetic Algorithm in Mutation Testing

On the Effectiveness of Using Elitist Genetic Algorithm in Mutation Testing

Mutation testing in the wild: findings from GitHub

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